Product renting method based on AI big data dialogue generation

Abstract

The invention discloses a dialogue generation type product leasing method based on AI big data, and the method comprises the steps: building a tagged database of commodity information and the like at a server side, enabling a user to initiate a leasing demand dialogue through an APP, an applet and the like on an intelligent terminal, enabling a system to automatically match commodities to form an order, transmitting the order to the user, and enabling the user to achieve the leasing demand through the direct payment. The system only recommends the most conforming commodity to the user, the process that the user selects the commodity in a database by browsing the commodity is omitted, and the user experience is greatly improved.

Description

Technical Field

[0001] The present invention relates to a method for recommending rental products, and specifically to a product rental method based on AI big data and dialogue generation. Background Art

[0002] In existing leasing transaction methods, users generally select the goods they want to rent through the leasing platform or the merchant's own mini-program. After confirming external factors such as the lease period and price, they manually submit and confirm the order information. After confirmation by the merchant, the relevant leasing service is completed. A Chinese patent (publication number CN118916552A) discloses a vehicle leasing information recommendation and reward settlement method and system based on user profiles. The method obtains the user's personal information and constructs a user profile based on historical leasing behavior data; analyzes the user's preference tags based on the user profile, and filters out vehicle information that meets the user's preference tags from the vehicle database; generates a recommendation list for the vehicle information that meets the user's preference tags through a recommendation algorithm and pushes it to the user; records the user's feedback on the recommendation list, updates the user profile, and adjusts the recommendation strategy; calculates the user's reward points based on the user's leasing behavior record using preset reward rules, and gives the user corresponding rewards based on the reward points. This technical solution improves the quality of recommendation results, but the process of users locking in rental products requires them to manually enter various preferences to gradually narrow down the range of products that meet their requirements. Users then select products that meet their needs from the many recommended products listed. The overall ordering process for users is still lengthy. Summary of the Invention

[0003] The purpose of the present invention is to provide a leasing method that directly sends a leasing order to the user through intelligent screening and recommendation based on the user's leasing needs and completes the transaction, thereby greatly shortening the user's ordering time and improving the user experience.

[0004] In order to achieve the purpose of the present invention, the following technical solutions are proposed:

[0005] A product leasing method based on AI big data and dialogue generation, comprising the following steps:

[0006] S1. On the server, the types of rentable goods and their attributes, rental rules, and user personal information are stored in a tagged database, and an API interface service is provided externally.

[0007] S2. The user sends the rental intention data to the server through the request terminal;

[0008] S3: The server system parses and understands the data sent by the user and stores it synchronously. Then, based on the parsed and understood information, it searches and matches the corresponding products in the database in S1, integrates the matching products with the rental rules, forms an order, and sends it to the user's request terminal.

[0009] S4. After the user browses the ordered items, if the order is confirmed, the user directly completes the payment and places the order; if the user believes that the ordered items do not meet the requirements, the user repeats the above steps S2-S3 until the order is confirmed;

[0010] S5. The merchant reviews the order, ships the goods, and the user receives the goods before the lease period begins. After the lease expires, the merchant pushes the return order information to the user. After the user confirms, the merchant places an order to pick up the goods through express delivery. The lease process ends after the merchant receives and confirms the goods.

[0011] In step S1, the system components, functional services, API interfaces, etc. are integrated into the mini-program, APP or WEB website, and the API interface services are provided externally for quick and direct calls by other products.

[0012] In step S1, the product attributes include brand, specific product model, lease term, rent payment method, lease deposit, shipping location, and courier service information, and form label information;

[0013] In step S2, the user initiates a conversation on the smart terminal through a mini-program, APP, or WEB website, and sends the rental intention to the server via text, voice, or pictures;

[0014] In step S3, specifically:

[0015] S31. Parse the rental intention data sent by the user and identify and capture the product and its attribute information;

[0016] S32. Sort the product types in the selected products by matching degree through search and comparison, and select the product type with the highest matching degree;

[0017] S33. Under the product type selected in S32, the product attributes of different products are scored for matching, and the scoring results of each attribute are sorted respectively;

[0018] S34. After assigning weights to the attribute information in S33 based on the personal information in the database, the product with the highest matching degree is selected from the attribute information, and the order is formed by integrating the rental rules and sent to the user.

[0019] In step S4, if it is a new user, the information that the user needs to supplement is automatically pushed, including the delivery address and real-name information.

[0020] In step S4, when the user makes payment, the user's personal credit is judged based on the ordering user's personal information, including credit, credit investigation, multiple leases, loan applications, legal proceedings and other information, to evaluate whether credit-free deposit leasing is possible. If the credit review is passed, the payment order process is entered; if the credit review is unsuccessful, the deposit that needs to be paid is pushed and entered into the payment order process together with the rent, or the lease termination process is directly rejected.

[0021] In step S5, if the rent is not paid in one lump sum, a rent payment order is sent to the user at an agreed time interval, and the user completes the payment through the order.

[0022] Compared with the existing technology, in this technical solution, on the user terminal, the user initiates a conversation and sends voice, text or pictures to the system. The system searches and matches the information sent by the user in the database, then selects the product with the highest matching degree and forms an order and sends it to the user terminal. The user proposes more product attribute information based on his or her own intentions. The system then continuously optimizes the matching results and feeds back to the user in the form of an order. After the user confirms the order, the lease period can begin. The entire process is completed in the form of a conversation, which saves the user from browsing the product in the database and selecting it, greatly improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Flowchart of an embodiment of the present invention.

[0024] Figure 2 This is the rendering effect when initiating a rental request on the user terminal. DETAILED DESCRIPTION

[0025] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0026] Combine Figure 1 The embodiment of the present invention provides a product leasing method based on AI big data dialogue generation, which is implemented by the following steps:

[0027] S1. On the server side, the types of goods that can be rented and their attributes, rental rules, user personal information, etc. are formed into a labeled database, and an API interface service is provided to the outside world; the goods and their attributes refer to detailed product information such as brand, specific product model, rental period, rental payment method, rental deposit, shipping location, and express service information, and the labeled information is formed for easy retrieval and matching.

[0028] S2. The user sends the rental intention data to the server through the request terminal; the request terminal is a smart device such as a mobile phone, tablet computer, etc., which is implemented through APP, mini-program, or WEB website. The content sent by the user is text, picture or voice.

[0029] S3. The data sent by the user is parsed and understood in the background of the server system, and stored synchronously. Then, based on the parsed and understood information, the corresponding products are retrieved and matched in the database in S1, and the matched products are integrated with the rental rules to form an order and sent to the user's request terminal. It should be noted that in the existing technology, the recognition technology for text, pictures or voice is already very mature, and this application does not elaborate on it.

[0030] S4. After the user browses the ordered items, if the order is confirmed, the payment is completed directly; if the user believes that the ordered items do not meet the requirements, the above S2-S3 steps are repeated until the order is confirmed; if the user is a new user, the user's supplementary information is automatically pushed, including the delivery address, real-name information, etc.

[0031] Combine Figure 2 In this embodiment, the user initiates a rental demand to the system in the form of text "rent a camera for outdoor bird photography for 2 days". The system determines the type of product to be rented based on the "camera" in the user's demand, and determines that the product model that meets the requirements of "outdoor bird photography" is "Nikon P1000", and determines the rental period to be 2 days. The determined information is integrated into an order and sent to the user. The user can click on the hyperlink of "Nikon P1000" to learn about the specific functions and parameters of the camera. The user believes that this model of camera cannot meet the shooting needs, and puts forward the demand of "a better camera with a telephoto lens" to the system. The system then screens out the "Canon EOS R5+RF100-400mm lens" that better meets the user's requirements, modifies the order information and resends it to the user. The user clicks "Pay" to complete the product rental.

[0032] When the user makes a payment, the user's personal credit is judged based on the ordering user's personal information, including credit, credit investigation, multiple leases, loan applications, legal proceedings and other information, to assess whether credit-free leasing is possible. If the credit review is passed, the payment and ordering process will be entered; if the credit review is unsuccessful, the deposit that needs to be paid will be pushed and entered into the payment and ordering process together with the rent, or the lease termination process will be directly rejected; personal credit judgment can also use data from a third-party credit platform.

[0033] S5. The merchant reviews the order, ships the goods, and the user receives them, and the lease period begins. Upon expiration of the lease, the merchant sends a return order to the user, who confirms the order and picks up the goods via courier. The lease ends after the merchant receives and confirms the goods. If the rent is not paid in full, the merchant will send a payment order to the user at agreed-upon intervals, such as monthly, quarterly, or annually, and the user completes the payment through the order.

[0034] The above are merely recommended embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A product leasing method based on AI big data and dialogue generation, characterized by: Including the above implementation steps: S1. On the server, the types of rentable goods and their attributes, rental rules, and user personal information are stored in a tagged database, and an API interface service is provided externally. S2. The user sends the rental intention data to the server through the request terminal; S3: The server system parses and understands the data sent by the user and stores it synchronously. Then, based on the parsed and understood information, it searches and matches the corresponding products in the database in S1, integrates the matching products with the rental rules, forms an order, and sends it to the user's request terminal. S4. After the user browses the ordered items, if the order is confirmed, the user directly completes the payment and places the order; if the user believes that the ordered items do not meet the requirements, the user repeats the above steps S2-S3 until the order is confirmed; S5. The merchant reviews the order, ships the goods, and the user receives the goods before the lease period begins. After the lease expires, the merchant pushes the return order information to the user. After the user confirms, the merchant places an order to pick up the goods through express delivery. The lease process ends after the merchant receives and confirms the goods.

2. The product leasing method based on AI big data dialogue generation according to claim 1 is characterized in that: In step S1, the system components, functional services, API interfaces, etc. are integrated into the mini-program, APP or WEB website, and the API interface services are provided externally for quick and direct calls by other products.

3. The product leasing method based on AI big data dialogue generation according to claim 1 is characterized in that: In step S1, the commodity attributes include brand, specific product model, lease term, rent payment method, lease deposit, shipping location, and courier service information, and form labeled information.

4. The product leasing method based on AI big data dialogue generation according to claim 1 is characterized in that: In step S2, the user initiates a conversation on the smart terminal through a mini-program, APP, or WEB website, and sends the rental intention to the server via text, voice, or pictures.

5. The product leasing method based on AI big data dialogue generation according to claim 1 is characterized in that: In step S3, specifically: S31. Parse the rental intention data sent by the user and identify and capture the product and its attribute information; S32. Sort the product types in the selected products by matching degree through search and comparison, and select the product type with the highest matching degree; S33. Under the product type selected in S32, the product attributes of different products are scored for matching, and the scoring results of each attribute are sorted respectively; S34. After assigning weights to the attribute information in S33 based on the personal information in the database, the product with the highest matching degree is selected from the attribute information, and the order is formed by integrating the rental rules and sent to the user.

6. The product leasing method based on AI big data dialogue generation according to claim 1 is characterized in that: In step S4, if it is a new user, the information that the user needs to supplement is automatically pushed, including the delivery address and real-name information.

7. The product leasing method based on AI big data dialogue generation according to claim 1 is characterized in that: In step S4, when the user makes payment, the user's personal credit is judged based on the ordering user's personal information, including credit, credit investigation, multiple leases, loan applications, legal proceedings and other information, to assess whether credit-free rental can be achieved. If the credit review is passed, the payment order process will be entered; If the credit review is unsuccessful, the deposit that needs to be paid will be pushed and entered into the payment order process together with the rent, or the lease termination process will be directly rejected.

8. The product leasing method based on AI big data dialogue generation according to claim 1 is characterized in that: In step S5, if the rent is not paid in one lump sum, a rent payment order is sent to the user at an agreed time interval, and the user completes the payment through the order.

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